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Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems
Abstract
The contribution of power production by photovoltaic (PV) systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production. This forecast information is necessary for the management of the electricity grids and for solar energy trading. This paper presents an approach to predict regional PV power output based on forecasts up to three days ahead provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Focus of the paper is the description and evaluation of the approach of irradiance forecasting, which is the basis for PV power prediction. One day-ahead irradiance forecasts for single stations in Germany show a rRMSE of 36%. For regional forecasts, forecast accuracy is increasing in dependency on the size of the region. For the complete area of Germany, the rRMSE amounts to 13%. Besides the forecast accuracy, also the specification of the forecast uncertainty is an important issue for an effective application. We present and evaluate an approach to derive weather specific prediction intervals for irradiance forecasts. The accuracy of PV power prediction is investigated in a case study.
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Cited by
Photovoltaic and solar power forecasting for smart grid energy management
Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. (2015) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2015.00046 · Cited by 654
Zheng, Y., Dong, Z. Y., Luo, F. J., Meng, K., Qiu, J., & Wong, K. P. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2278850 · Cited by 320
Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions
Andrade, J. R., & Bessa, R. J. (2017) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2017.2694340 · Cited by 292
Integration of Renewable Energy into Present and Future Energy Systems
Sims, R., Mercado, P., Krewitt, W., Bhuyan, G., Flynn, D., Holttinen, H., Jannuzzi, G., Khennas, S., Liu, Y., Nilsson, L. J., et al. (2011) · Renewable Energy Sources and Climate Change Mitigation · doi:10.1017/cbo9781139151153.012 · Cited by 98
Metadata
- Authors
- Lorenz, E., Hurka, J., Heinemann, D., & Beyer, H. G.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2009-03
- Volume
- 2
- Issue
- 1
- Pages
- 2-10
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2099365903
- Cited by
- 721
- References
- 0
- FWCI
- 26.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lorenz, E., Hurka, J., Heinemann, D., & Beyer, H. G. (2009). Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2009.2020300
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